The main piece of previous work on question generation for fact-checking is by [1]}. They proposed the QABriefs dataset, which consists of claims with manually annotated question-answer pairs containing additional information about the claims (e.g. the exact definition of a term, the content of a bill, details about a political statement or a vote). The QABriefs dataset contained questions asked by crowd-workers, who had to read both the claim and its fact-checking article. [1]} presented the QABriefer model, which generates a set of questions conditioned on the claim, searches the web for evidence and retrieves answers.
However, they evaluated the questions generated only using BLEU scores without conducting a human evaluation. More recently, [3]} addressed the problem of explainability in fact-checking through question answering using the Fool Me Twice corpus [4]}. They generated questions from the claim, retrieved answers from the evidence, and compared them to the generated ones.
Yet, they did not evaluate their question generation process, and assumed that the evidence is given as input to generate the questions, which is unrealistic since the questions are typically used for evidence retrieval.
